1. Field of the Invention
The present invention relates in general to the field of information processing, and more specifically to a system and method for using data from user sessions to determine product demand. The system and method also has the capability to determine demand of product features with high-resolution.
2. Description of the Related Art
Manufacturers often attempt to determine demand for certain products. In many industries, such as the automotive industry, a significant time delay exists between when a product should be specified for manufacture and when the product is available for sale. Manufacturers risk a significant amount of revenue based on forecasted demand. For example, consider a truck manufacturer who builds 25% of the trucks with 4×4 drive trains and 75% with 4×2 drive trains based on an inaccurate forecast. The manufacturer and dealers typically lose a significant amount of money if the actual demand for 4×4 versus 4×2 trucks is more than a few percentage points different than forecasted. Even if the profit margins on 4×4 and 4×2 trucks are the same, a dealer may be forced to discount a 4×4 truck to a buyer who preferred a 4×2 truck and vice versa. Therefore, manufacturers who base manufactured products on forecasted demand place a high value in accurate product demand forecasts.
Unfortunately, consistently forecasting accurate demand remains an elusive goal. In the above example, the truck manufacturer divided truck production between 4×2 trucks and 4×4 trucks. In reality, the set of products is often very large, where each particular factory configuration of a product is considered a separate product. For example, not only do manufacturers forecast drive trains, they also forecast a large number of other configuration alternatives such as exterior and interior colors, engine sizes, body styles, seat numbers, option packages, and wheel type. The increasing number of configuration options renders consistently accurate forecasting difficult.
In almost all circumstances, manufactures use actual dealer sales data to forecast demand. However, historically such data is often not a good predictor of future buying trends. The primary reason for such inaccuracy is often referred to as the “white car problem”. If a buyer prefers a car and the dealer only has a white car but no red car, the buyer may purchase the white car. However, often the dealer enticed the buyer with incentives such as vehicle discounts, extra options at no additional charge, and/or a higher trade-in allowance. Thus, the actual sales data did not accurately represent buyer demand and will likely misrepresent forecasted demand as well.